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A modified matrix adaptation evolution strategy with restarts for constrained real-world problems
(2020)
In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the εMAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-εMAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original εMAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-εMAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.
With Cloud Computing and multi-core CPUs parallel computing resources are becoming more and more affordable and commonly available. Parallel programming should as well be easily accessible for everyone. Unfortunately, existing frameworks and systems are powerful but often very complex to use for anyone who lacks the knowledge about underlying concepts. This paper introduces a software framework and execution environment whose objective is to provide a system which should be easily usable for everyone who could benefit from parallel computing. Some real-world examples are presented with an explanation of all the steps that are necessary for computing in a parallel and distributed manner.
The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual use of property-based testing. In this code testing method, model specifications and invariants are expressed directly in code and tested through automated and randomised test data generation. This paper expands on our previous work and explores how to use property-based testing on a technical level to encode and test specifications of ABS. As use case the simple agent-based SIR model is used, where it is shown how to test agent behaviour, transition probabilities and model invariants. The outcome are specifications expressed directly in code, which relate whole classes of random input to expected classes of output. During test execution, random test data is generated automatically, potentially covering the equivalent of thousands of unit tests, run within seconds on modern hardware. This makes property-based testing in the context of ABS strictly more powerful than unit testing, as it is a much more natural fit due to its stochastic nature.